Paper Info
Title
Combinatorial effects of local structures and scoring metrics in bayesian optimization algorithm
Abstract
Bayesian Optimization Algorithm (BOA) has been used with different local structures to represent more complex models and a variety of scoring metrics to evaluate Bayesian network. But the combinatorial effects of these elements on the performance of BOA have not been investigated yet. In this paper the performance of BOA is studied using two criteria: Number of fitness evaluations and structural accuracy of the model. It is shown that simple exact local structures like CPT in conjunction with complexity penalizing BIC metric outperforms others in terms of model accuracy. But considering number of fitness evaluations (efficiency) of the algorithm, CPT with other complexity penalizing metric K2P performs better.
Year
DOI
Venue
2009
10.1145/1543834.1543870
GEC Summit
Keywords
Field
DocType
complex model,metric k2p,bayesian network,bayesian optimization algorithm,simple exact local structure,fitness evaluation,structural accuracy,combinatorial effect,model accuracy,bic metric,scoring metrics,different local structure,estimation of distribution algorithms,estimation of distribution algorithm
Mathematical optimization,Estimation of distribution algorithm,Computer science,Bayesian network,Artificial intelligence,Bayesian optimization algorithm,Machine learning
Conference
Citations 
PageRank 
References 
5
0.44
9
Authors
4
Name
Order
Citations
PageRank
Hossein Karshenas11477.79
Amin Nikanjam2628.94
B. Hoda Helmi3224.24
Adel Torkaman Rahmani413919.77